def test_out_scaling_alpha_learning_columnwise(self):
"""Check if out scaling alpha are learning."""
rpu_config = InferenceRPUConfig(
mapping=MappingParameter(weight_scaling_omega=0.6,
learn_out_scaling_alpha=True,
weight_scaling_omega_columnwise=True))
analog_model = Sequential(
self.get_layer(in_channels=2,
out_channels=2,
kernel_size=4,
padding=2,
rpu_config=rpu_config),
self.get_layer(in_channels=2,
out_channels=3,
kernel_size=4,
padding=2,
rpu_config=rpu_config))
loss_func = mse_loss
y_b = randn(3, 3, 6, 6, 6)
x_b = randn(3, 2, 4, 4, 4)
if self.use_cuda:
y_b = y_b.cuda()
x_b = x_b.cuda()
initial_out_scaling_alpha_0 = analog_model[
0].analog_tile.get_out_scaling_alpha().clone()
initial_out_scaling_alpha_1 = analog_model[
1].analog_tile.get_out_scaling_alpha().clone()
self.train_model(analog_model, loss_func, x_b, y_b)
learned_out_scaling_alpha_0 = analog_model[
0].analog_tile.get_out_scaling_alpha().clone()
learned_out_scaling_alpha_1 = analog_model[
1].analog_tile.get_out_scaling_alpha().clone()
self.assertGreaterEqual(initial_out_scaling_alpha_0.numel(), 1)
self.assertIsNotNone(
analog_model[0].analog_tile.get_out_scaling_alpha().grad)
self.assertNotAlmostEqualTensor(initial_out_scaling_alpha_0,
learned_out_scaling_alpha_0)
self.assertGreaterEqual(initial_out_scaling_alpha_1.numel(), 1)
self.assertIsNotNone(
analog_model[1].analog_tile.get_out_scaling_alpha().grad)
self.assertNotAlmostEqualTensor(initial_out_scaling_alpha_1,
learned_out_scaling_alpha_1)
def test_out_scaling_alpha_learning_columnwise(self):
"""Check if out scaling alpha are learning when columnwise is True."""
loss_func = mse_loss
x_b = Tensor([[0.1, 0.2, 0.3, 0.4], [0.2, 0.4, 0.3, 0.1]])
y_b = Tensor([[0.3], [0.6]])
manual_seed(4321)
rpu_config = InferenceRPUConfig(
mapping=MappingParameter(weight_scaling_omega=0.6,
learn_out_scaling_alpha=True,
weight_scaling_omega_columnwise=True))
model = Sequential(self.get_layer(4, 2, rpu_config=rpu_config),
self.get_layer(2, 1, rpu_config=rpu_config))
if self.use_cuda:
x_b = x_b.cuda()
y_b = y_b.cuda()
model = model.cuda()
initial_out_scaling_alpha_0 = model[
0].analog_tile.get_out_scaling_alpha().clone()
initial_out_scaling_alpha_1 = model[
1].analog_tile.get_out_scaling_alpha().clone()
self.train_model(model, loss_func, x_b, y_b)
learned_out_scaling_alpha_0 = model[
0].analog_tile.get_out_scaling_alpha().clone()
learned_out_scaling_alpha_1 = model[
1].analog_tile.get_out_scaling_alpha().clone()
self.assertGreaterEqual(initial_out_scaling_alpha_0.numel(), 1)
self.assertIsNotNone(model[0].analog_tile.get_out_scaling_alpha().grad)
self.assertNotAlmostEqualTensor(initial_out_scaling_alpha_0,
learned_out_scaling_alpha_0)
self.assertGreaterEqual(initial_out_scaling_alpha_1.numel(), 1)
self.assertIsNotNone(model[1].analog_tile.get_out_scaling_alpha().grad)
self.assertNotAlmostEqualTensor(initial_out_scaling_alpha_1,
learned_out_scaling_alpha_1)
